ArticleNature methods2023
Comparison of transformations for single-cell RNA-seq data.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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Who cites it
98 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain.Scientific reports · 2023Pooled it
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- High-throughput evaluation of in vitro CRISPR activities enables optimized large-scale multiplex enrichment of rare variants.Nature biomedical engineering · 2026Article
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer.Nature communications · 2026Article
- Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions.bioRxiv : the preprint server for biology · 2026Article
- Network clustering algorithms and preprocessing pipelines for robust cell type identification in single-cell RNA sequencing data.Scientific reports · 2026Article
- Integrating theory and machine learning to reveal determinants of plasmid copy number.Nature communications · 2026Article
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- Single-cell, clonal and spatial atlases of cranial placodes illuminate their specification and evolution.bioRxiv : the preprint server for biology · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Hidden immune memory niches in inflammatory skin diseases.bioRxiv : the preprint server for biology · 2026Article
- Cellular and molecular landscapes of human tendons across the lifespan revealed by spatial and single-cell transcriptomics.Cell reports · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq.bioRxiv : the preprint server for biology · 2026Article
- A highly resolved integrated single-cell atlas of human breast cancers.NAR genomics and bioinformatics · 2026Article
38 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.